Eko

// Overview

Eko (pronounced like 'echo') is a JavaScript framework for building agents that go from a single natural-language instruction to a multi-step workflow. Its distinguishing choice is the runtime target: rather than being a server-side Python library, Eko is pure JavaScript designed to run in both computer and browser environments — Node.js, a web page, or a Chrome extension — behind one unified interface.

A task is described in a sentence and planned into a workflow the framework executes, with agents composed from built-in pieces such as `BrowserAgent` and `FileAgent` or custom ones you define. Version 3.0 added dependency-aware parallel agent execution so independent branches run at the same time, plus pause, resume and interrupt controls with `task_snapshot` recovery; version 4.0 added chat conversations and reworked the agent logic.

Models are configured per-task as a map, so a workflow can mix providers — Anthropic, Google and any OpenAI-compatible endpoint including Qwen via DashScope or Doubao via Volcengine — and pick different models for different steps. MCP servers connect natively for tools, and human-in-the-loop hooks let a person intervene at the points that matter. The project warns explicitly against putting API keys in browser or front-end code: configure a backend proxy via `baseURL` and request headers instead.

// What it does

• Pure JavaScript, built for both Node.js and browsers — including Chrome-extension deployment
• One sentence to a multi-step workflow, with stream planning for dynamic rendering as the plan forms
• Dependency-aware parallel execution of independent agent branches (Eko 3.0)
• Pause, resume and interrupt controls with `task_snapshot` workflow recovery
• Multi-agent composition with built-in `BrowserAgent` and `FileAgent`, plus custom agents and tools in a line of code
• Dynamic LLM selection: a per-task map of providers, so different steps can use different models
• Native MCP support for connecting external tool servers
• Human-in-the-loop hooks to intervene mid-workflow

// Getting started

Eko installs as a set of npm packages and is driven from TypeScript. The repository ships three workspace examples under `example/` — a browser extension, a Node.js automation demo and a web login demo.

pnpm install @eko-ai/eko

const llms: LLMs = {
  default: {
    provider: "anthropic",
    model: "claude-sonnet-4-5-20250929",
    apiKey: "your-api-key"
  },
  qwen: {
    provider: "openai",
    model: "qwen-plus",
    apiKey: "your-qwen-api-key",
    config: {
      baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
  }
};

let agents: Agent[] = [new BrowserAgent(), new FileAgent()];
let eko = new Eko({ llms, agents });
let result = await eko.run("Search for the latest news about Musk, summarize and save to the desktop as Musk.md");

pnpm install
pnpm build

cd example/nodejs
pnpm install
pnpm playwright install   # first time only, installs browsers
pnpm run build
OPENAI_API_KEY=... ANTHROPIC_API_KEY=... pnpm run start

cd example/extension
pnpm install
pnpm run build
# chrome://extensions → Developer Mode → Load unpacked → select dist/

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Browser automation and web scraping driven by a natural-language instruction rather than a hand-written script
• Shipping an agent inside a Chrome extension, where a Python framework cannot run
• Workflows whose independent steps should run in parallel, with the framework resolving the dependency graph
• Long-running automation that a person needs to pause, inspect and resume rather than watch run to completion

// How Eko compares

Eko alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.